Literatura académica sobre el tema "ARRHYTHMIA DATABASE"
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Artículos de revistas sobre el tema "ARRHYTHMIA DATABASE"
CHIU, CHUANG-CHIEN, TONG-HONG LIN y BEN-YI LIAU. "USING CORRELATION COEFFICIENT IN ECG WAVEFORM FOR ARRHYTHMIA DETECTION". Biomedical Engineering: Applications, Basis and Communications 17, n.º 03 (25 de junio de 2005): 147–52. http://dx.doi.org/10.4015/s1016237205000238.
Texto completoZhai, Yuyun, Jinwei Li y Quan Zhang. "Network pharmacology and molecular docking analyses of the potential target proteins and molecular mechanisms underlying the anti-arrhythmic effects of Sophora Flavescens". Medicine 102, n.º 30 (28 de julio de 2023): e34504. http://dx.doi.org/10.1097/md.0000000000034504.
Texto completoDeal, Barbara J., Constantine Mavroudis, Jeffrey Phillip Jacobs, Melanie Gevitz y Carl Lewis Backer. "Arrhythmic complications associated with the treatment of patients with congenital cardiac disease: consensus definitions from the Multi-Societal Database Committee for Pediatric and Congenital Heart Disease". Cardiology in the Young 18, S2 (diciembre de 2008): 202–5. http://dx.doi.org/10.1017/s104795110800293x.
Texto completoMoreland-Head, Lindsay N., James C. Coons, Amy L. Seybert, Matthew P. Gray y Sandra L. Kane-Gill. "Use of Disproportionality Analysis to Identify Previously Unknown Drug-Associated Causes of Cardiac Arrhythmias Using the Food and Drug Administration Adverse Event Reporting System (FAERS) Database". Journal of Cardiovascular Pharmacology and Therapeutics 26, n.º 4 (6 de enero de 2021): 341–48. http://dx.doi.org/10.1177/1074248420984082.
Texto completoZeng, Yuni, Hang Lv, Mingfeng Jiang, Jucheng Zhang, Ling Xia, Yaming Wang y Zhikang Wang. "Deep arrhythmia classification based on SENet and lightweight context transform". Mathematical Biosciences and Engineering 20, n.º 1 (2022): 1–17. http://dx.doi.org/10.3934/mbe.2023001.
Texto completoKapoor, Ankita, Samarthkumar Thakkar, Lucas Battel, Harsh P. Patel, Nikhil Agrawal, Shipra Gandhi, Pritika Manaktala et al. "The Prevalence and Impact of Arrhythmias in Hospitalized Patients with Sickle Cell Disorders: A Large Database Analysis". Blood 136, Supplement 1 (5 de noviembre de 2020): 5–6. http://dx.doi.org/10.1182/blood-2020-142099.
Texto completoOTHMAN, MOHD AFZAN y NORLAILI MAT SAFRI. "CHARACTERIZATION OF VENTRICULAR ARRHYTHMIAS USING A SEMANTIC MINING ALGORITHM". Journal of Mechanics in Medicine and Biology 12, n.º 03 (junio de 2012): 1250049. http://dx.doi.org/10.1142/s0219519412004946.
Texto completoXu, Gang, Guangxin Xing, Juanjuan Jiang, Jian Jiang y Yongsheng Ke. "Arrhythmia Detection Using Gated Recurrent Unit Network with ECG Signals". Journal of Medical Imaging and Health Informatics 10, n.º 3 (1 de marzo de 2020): 750–57. http://dx.doi.org/10.1166/jmihi.2020.2928.
Texto completoN. S. V Rama Raju, N., V. Malleswara Rao y I. Srinivasa Rao. "Automatic detection and classification of cardiac arrhythmia using neural network". International Journal of Engineering & Technology 7, n.º 3 (11 de julio de 2018): 1482. http://dx.doi.org/10.14419/ijet.v7i3.14084.
Texto completoHerman, Jeffrey N., Richard I. Fogel, Philip J. Podrid y Gary R. Garber. "Entropy: A cardiac arrhythmia multimedia database". Journal of the American College of Cardiology 17, n.º 2 (febrero de 1991): A10. http://dx.doi.org/10.1016/0735-1097(91)91008-3.
Texto completoTesis sobre el tema "ARRHYTHMIA DATABASE"
Engström, Magnus y Nadia Soheily. "EKG-analys och presentation". Thesis, KTH, Data- och elektroteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-154539.
Texto completoThe interpretation of the ECG is an important method in the diagnosis of abnormal heart conditions and can be used proactively to discover previ-ously unknown heart problems. Being able to easily measure the ECG and get it analyzed and presented in a clear manner without having to consult a doctor is improtant to satisfy consumer needs. This report describes how an ECG signal is treated with different algo-rithms and methods to detect the heartbeat and its various parameters. This information is used to classify each heartbeat separately and thus determine whether the user has a normal or abnormal cardiac function. To achieve this a software prototype was developed in which the algorithms were implemented. A questionnaire survey was done in order to examine how the output of the software prototype should be presented for a user with no medical training. Seven ECG files from MIT-BIH Arrhythmia database were used for validation of the algorithms. The developed algorithms could detect of if any abnormality of heart function occurred and informed the users to consult a physician. The presentation of the heart function was based on the result from the questioner.
Bsoul, Abed Al-Raoof. "PROCESSING AND CLASSIFICATION OF PHYSIOLOGICAL SIGNALS USING WAVELET TRANSFORM AND MACHINE LEARNING ALGORITHMS". VCU Scholars Compass, 2011. http://scholarscompass.vcu.edu/etd/258.
Texto completoZhorný, Lukáš. "Detekce komplexů QRS v signálech EKG". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-413175.
Texto completoMUNJAL, NAVEEN KUMAR. "ECG DENOISING USING THE WAVELETS AND ROBUST ANALYSIS OF ECG SIGNALS". Thesis, 2013. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15780.
Texto completoKuo, Yi-Shi y 郭怡希. "Use of Cardiac Arrhythmia Interpretation Timing Characteristics Related Diseases Characterized by Solid Research and Database for Hadoop". Thesis, 2015. http://ndltd.ncl.edu.tw/handle/uhc4gm.
Texto completo國立中正大學
通訊資訊數位學習碩士在職專班
103
Abstract This article aims to propose an interpretation method that utilizes time sequence characteristics in order to classify the symptoms of heart diseases, as well as to store the voluminous data before and after classification into the database via the parallel algorithm approach, in order to facilitate the utilization of future medical therapy. Those issues to be faced are as follows: the initial one is to obtain a PR interval and to use the change of this time sequence as input data for the identification between normal rhythm and abnormal rhythm of cardiac arrhythmia. The waveforms identified by a classifier include the normal rhythm, cardiac arrhythmia and others. The data of ECG signals are from the database of MIT-BIH Arrhythmia with selected 5-file data of heartbeat periods integrated with the LIBSVM Function and algorithm of Professor Lin Chih-Jen. The time sequence characteristics can still have an almost 100% accuracy rate under the influence of sound. Also, the characteristic points are computed as to the hyper-plane distance and the relationships between those accuracy rates are investigated.
Zhao, Hui. "Magnetocardiographic evaluation of fetal arrhythmia /". 2005. http://www.library.wisc.edu/databases/connect/dissertations.html.
Texto completoSilva, Aurélio Filipe de Sousa e. "Deteção de extra-sístoles ventriculares". Master's thesis, 2012. http://hdl.handle.net/10216/68387.
Texto completoSilva, Aurélio Filipe de Sousa e. "Deteção de extra-sístoles ventriculares". Dissertação, 2012. http://hdl.handle.net/10216/68387.
Texto completoVega, Amanda L. "Arrhythmia mutations in the cardiac inward rectifying potassium channel Kir2.1 (KCNJ2) : mechanisms for molecular and cellular phenotypes /". 2008. http://www.library.wisc.edu/databases/connect/dissertations.html.
Texto completoCapítulos de libros sobre el tema "ARRHYTHMIA DATABASE"
Kuila, Sumanta, Namrata Dhanda y Subhankar Joardar. "Feature Extraction and Classification of MIT-BIH Arrhythmia Database". En Lecture Notes in Electrical Engineering, 417–27. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0829-5_41.
Texto completoMartono, Niken Prasasti, Toru Nishiguchi y Hayato Ohwada. "ECG Signal Classification Using Recurrence Plot-Based Approach and Deep Learning for Arrhythmia Prediction". En Intelligent Information and Database Systems, 327–35. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21743-2_26.
Texto completoZhang, Jingyao, Fengying Ma y Wei Chen. "An Improved CNNLSTM Algorithm for Automatic Detection of Arrhythmia Based on Electrocardiogram Signal". En Database Systems for Advanced Applications. DASFAA 2021 International Workshops, 185–96. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73216-5_13.
Texto completoMontenegro, Larissa, Hugo Peixoto y José M. Machado. "Evaluation of Transfer Learning to Improve Arrhythmia Classification for a Small ECG Database". En Advances in Artificial Intelligence – IBERAMIA 2022, 231–42. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-22419-5_20.
Texto completoTorres-Alegre, Santiago, Juan Fombellida, Juan Antonio Piñuela-Izquierdo y Diego Andina. "Artificial Metaplasticity: Application to MIT-BIH Arrhythmias Database". En Artificial Computation in Biology and Medicine, 133–42. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18914-7_14.
Texto completoTravieso, Carlos M., Jesús B. Alonso, Miguel A. Ferrer y Jorge Corsino. "Automatic Arrhythmia Detection". En Soft Computing Methods for Practical Environment Solutions, 204–18. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-893-7.ch013.
Texto completoJha, Chandan Kumar. "ECG Signal Analysis for Automated Cardiac Arrhythmia Detection". En Advances in Bioinformatics and Biomedical Engineering, 140–57. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3947-0.ch008.
Texto completoEl Omary, Sara, Souad Lahrache y Rajae El Ouazzani. "A Lightweight CNN to Identify Cardiac Arrhythmia Using 2D ECG Images". En AI Applications for Disease Diagnosis and Treatment, 122–60. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-2304-2.ch005.
Texto completoJha, Chandan Kumar y Maheshkumar H. Kolekar. "Arrhythmia ECG Beats Classification Using Wavelet-Based Features and Support Vector Machine Classifier". En Advances in Medical Technologies and Clinical Practice, 74–88. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7796-6.ch004.
Texto completoN., Raghu. "Arrhythmia Detection Based on Hybrid Features of T-Wave in Electrocardiogram". En Advances in Systems Analysis, Software Engineering, and High Performance Computing, 1–20. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1192-3.ch001.
Texto completoActas de conferencias sobre el tema "ARRHYTHMIA DATABASE"
Wu, Meng-Hsi y Edward Y. Chang. "DeepQ Arrhythmia Database". En MM '17: ACM Multimedia Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3132635.3132647.
Texto completoBaia, Alexandre Farias y Adriana Rosa Garcez Castro. "A Competitive Structure of Convolutional Autoencoder Networks for Electrocardiogram Signals Classification". En XV Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/eniac.2018.4446.
Texto completoMerdjanovska, E. y A. Rashkovska. "Cross-Database Generalization of Deep Learning Models for Arrhythmia Classification". En 2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO). IEEE, 2021. http://dx.doi.org/10.23919/mipro52101.2021.9596930.
Texto completoŘedina, "Richard, Jakub Hejc, David Pospisil, Marina Ronzhina, Petra Novotna y Zdenek Starek". "Arrhythmia Database with Annotated Intracardial Atrial Signals from Pediatric Patients Undergoing Catheter Ablation". En 2022 Computing in Cardiology Conference. Computing in Cardiology, 2022. http://dx.doi.org/10.22489/cinc.2022.282.
Texto completoOliveira, Gustavo Henrique de y Franklin César Flores. "Classification of heart arrhythmia by digital image processing and machine learning". En Seminário Integrado de Software e Hardware. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/semish.2023.230225.
Texto completoTsoutsouras, Vasileios, Dimitra Azariadi, Sotirios Xydis y Dimitrios Soudris. "Effective Learning and Filtering of Faulty Heart-Beats for Advanced ECG Arrhythmia Detection using MIT-BIH Database". En 5th EAI International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies". ICST, 2015. http://dx.doi.org/10.4108/eai.14-10-2015.2261640.
Texto completoChakroborty, Sandipan y Meru A. Patil. "Real-time arrhythmia classification for large databases". En 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2014. http://dx.doi.org/10.1109/embc.2014.6943873.
Texto completoPoigai Arunachalam, Shivaram, Elizabeth M. Annoni, Suraj Kapa, Siva K. Mulpuru, Paul A. Friedman y Elena G. Tolkacheva. "Robust Discrimination of Normal Sinus Rhythm and Atrial Fibrillation on ECG Using a Multiscale Frequency Technique". En 2017 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dmd2017-3302.
Texto completoManilo, Liudmila A., Anatoly P. Nemirko, Ekaterina G. Evdakova y Anna A. Tatarinova. "ECG Database for Evaluating the Efficiency of Recognizing Dangerous Arrhythmias". En 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB). IEEE, 2021. http://dx.doi.org/10.1109/csgb53040.2021.9496029.
Texto completoWei Heng, Wei, Eileen Su Lee Ming, Ahmad Nizar Jamaluddin, Fauzan Khairi Che Harun, Nurul Ashikin Abdul-Kadir y Che Fai Yeong. "Prediction Algorithm of Malignant Ventricular Arrhythmia Validated across Multiple Online Public Databases". En 2019 Computing in Cardiology Conference. Computing in Cardiology, 2019. http://dx.doi.org/10.22489/cinc.2019.295.
Texto completoInformes sobre el tema "ARRHYTHMIA DATABASE"
Treadwell, Jonathan R., James T. Reston, Benjamin Rouse, Joann Fontanarosa, Neha Patel y Nikhil K. Mull. Automated-Entry Patient-Generated Health Data for Chronic Conditions: The Evidence on Health Outcomes. Agency for Healthcare Research and Quality (AHRQ), marzo de 2021. http://dx.doi.org/10.23970/ahrqepctb38.
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